Organizations often face the challenge of making decisions today in preparation for future events. These future events are variable in nature and oftentimes difficult to predict. Examples of these future variables include demand for a product, weather, consumer confidence, commodity prices, interest rates, as well as many others. To aid in making present decisions in preparation for unknown future scenarios, organizations utilize forecasting and estimation models to examine future states of these variables.
Organizational data can be expressed as hierarchical data structures. For example, a global retail sales company may have its stores organized by country, then by region, then by city, and then by individual store. In a further example, sales data for a product may be stored by year, then by quarter, then by month, and then by day. In keeping records, organizations may store the hierarchically arranged data in a multidimensional data store such as a multidimensional database or a relational database adapted to handle hierarchical data. These data hierarchies stored according to the requirements of an organization are called physical hierarchies. They may also be referred to as organizational or standard hierarchies. While these physical hierarchies are arranged to satisfy certain data storage needs of an organization, the physical hierarchies may often not be adequate to handle complex forecasting and estimation problems.